Wentao Liu1,2, Xuna Zhao1,2, Yajun Ma1,2, Xin Tang1,2, Jia-Hong Gao1,2,3. 1. Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China. 2. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China. 3. McGovern Institute for Brain Research, Peking University, Beijing, China.
Abstract
PURPOSE: A novel k-space reconstruction method is proposed for generating diffusion-weighted imaging (DWI) using navigated interleaved multishot EPI (msEPI). THEORY AND METHODS: In interleaved msEPI, each shot of data acquired from one coil channel is a subset of the full k-space of that channel. All the k-space subsets of one channel can be treated as an undersampled dataset of a virtual multichannel data, which can be reconstructed by the GRAPPA algorithm after k-space realignment. The intershot phase variations are directly compensated using navigator echoes as the auto-calibrating data in GRAPPA reconstruction. In cases of multichannel msEPI data, all the virtual channels and actual channels can be integrated into a single GRAPPA reconstruction step. The proposed method is tested using both simulation and in-vivo data. The simulation results produced by the proposed method and a SENSE-based method are compared. RESULTS: The simulated images generated by the proposed method exhibit less relative error compared with those generated by the SENSE method. Inconsistent shot-to-shot phase variation is naturally resolved by GRAPPA calibration without additional phase map processing. High-quality brain DWI with submillimeter resolution is obtained using our proposed reconstruction method. CONCLUSION: A novel k-space msEPI reconstruction method has been developed for generating high-quality diffusion imaging.
PURPOSE: A novel k-space reconstruction method is proposed for generating diffusion-weighted imaging (DWI) using navigated interleaved multishot EPI (msEPI). THEORY AND METHODS: In interleaved msEPI, each shot of data acquired from one coil channel is a subset of the full k-space of that channel. All the k-space subsets of one channel can be treated as an undersampled dataset of a virtual multichannel data, which can be reconstructed by the GRAPPA algorithm after k-space realignment. The intershot phase variations are directly compensated using navigator echoes as the auto-calibrating data in GRAPPA reconstruction. In cases of multichannel msEPI data, all the virtual channels and actual channels can be integrated into a single GRAPPA reconstruction step. The proposed method is tested using both simulation and in-vivo data. The simulation results produced by the proposed method and a SENSE-based method are compared. RESULTS: The simulated images generated by the proposed method exhibit less relative error compared with those generated by the SENSE method. Inconsistent shot-to-shot phase variation is naturally resolved by GRAPPA calibration without additional phase map processing. High-quality brain DWI with submillimeter resolution is obtained using our proposed reconstruction method. CONCLUSION: A novel k-space msEPI reconstruction method has been developed for generating high-quality diffusion imaging.
Authors: Berkin Bilgic; Itthi Chatnuntawech; Mary Kate Manhard; Qiyuan Tian; Congyu Liao; Siddharth S Iyer; Stephen F Cauley; Susie Y Huang; Jonathan R Polimeni; Lawrence L Wald; Kawin Setsompop Journal: Magn Reson Med Date: 2019-05-20 Impact factor: 4.668
Authors: Berkin Bilgic; Tae Hyung Kim; Congyu Liao; Mary Kate Manhard; Lawrence L Wald; Justin P Haldar; Kawin Setsompop Journal: Magn Reson Med Date: 2018-01-10 Impact factor: 4.668
Authors: Mark D Grier; Essa Yacoub; Gregor Adriany; Russell L Lagore; Noam Harel; Ru-Yuan Zhang; Christophe Lenglet; Kâmil Uğurbil; Jan Zimmermann; Sarah R Heilbronner Journal: Neuroimage Date: 2022-04-13 Impact factor: 7.400